{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2023-12-30T14:37:22.488739Z",
     "start_time": "2023-12-30T14:37:22.465729400Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction import DictVectorizer\n",
    "from xgboost import XGBClassifier\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"D:\\\\data\\\\titanic\\\\train.csv\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-30T13:32:49.646619500Z",
     "start_time": "2023-12-30T13:32:49.636629300Z"
    }
   },
   "id": "89e2995bcc865cb1"
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "       PassengerId    Survived      Pclass         Age       SibSp  \\\ncount   891.000000  891.000000  891.000000  714.000000  891.000000   \nmean    446.000000    0.383838    2.308642   29.699118    0.523008   \nstd     257.353842    0.486592    0.836071   14.526497    1.102743   \nmin       1.000000    0.000000    1.000000    0.420000    0.000000   \n25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n75%     668.500000    1.000000    3.000000   38.000000    1.000000   \nmax     891.000000    1.000000    3.000000   80.000000    8.000000   \n\n            Parch        Fare  \ncount  891.000000  891.000000  \nmean     0.381594   32.204208  \nstd      0.806057   49.693429  \nmin      0.000000    0.000000  \n25%      0.000000    7.910400  \n50%      0.000000   14.454200  \n75%      0.000000   31.000000  \nmax      6.000000  512.329200  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>PassengerId</th>\n      <th>Survived</th>\n      <th>Pclass</th>\n      <th>Age</th>\n      <th>SibSp</th>\n      <th>Parch</th>\n      <th>Fare</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>891.000000</td>\n      <td>891.000000</td>\n      <td>891.000000</td>\n      <td>714.000000</td>\n      <td>891.000000</td>\n      <td>891.000000</td>\n      <td>891.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>446.000000</td>\n      <td>0.383838</td>\n      <td>2.308642</td>\n      <td>29.699118</td>\n      <td>0.523008</td>\n      <td>0.381594</td>\n      <td>32.204208</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>257.353842</td>\n      <td>0.486592</td>\n      <td>0.836071</td>\n      <td>14.526497</td>\n      <td>1.102743</td>\n      <td>0.806057</td>\n      <td>49.693429</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>0.420000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>223.500000</td>\n      <td>0.000000</td>\n      <td>2.000000</td>\n      <td>20.125000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>7.910400</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>446.000000</td>\n      <td>0.000000</td>\n      <td>3.000000</td>\n      <td>28.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>14.454200</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>668.500000</td>\n      <td>1.000000</td>\n      <td>3.000000</td>\n      <td>38.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>31.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>891.000000</td>\n      <td>1.000000</td>\n      <td>3.000000</td>\n      <td>80.000000</td>\n      <td>8.000000</td>\n      <td>6.000000</td>\n      <td>512.329200</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-30T13:32:49.682580700Z",
     "start_time": "2023-12-30T13:32:49.649618400Z"
    }
   },
   "id": "8683c898c15edbfa"
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "x = data[[\"Pclass\", \"Sex\", \"Age\"]]\n",
    "y = data[[\"Survived\"]]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-30T13:32:49.692787900Z",
     "start_time": "2023-12-30T13:32:49.682580700Z"
    }
   },
   "id": "422609981e23c503"
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\AppData\\Local\\Temp\\ipykernel_11372\\3839002363.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  x[\"Age\"].fillna(value=data[\"Age\"].mean(), inplace=True)\n"
     ]
    }
   ],
   "source": [
    "# 对空的年龄用平均值填充\n",
    "x[\"Age\"].fillna(value=data[\"Age\"].mean(), inplace=True)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-30T13:32:49.694786600Z",
     "start_time": "2023-12-30T13:32:49.689574400Z"
    }
   },
   "id": "cd010c411c951758"
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [],
   "source": [
    "trainX, testX, trainY, testY = train_test_split(x, y, random_state=22, test_size=0.2)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-30T13:34:41.737954900Z",
     "start_time": "2023-12-30T13:34:41.722414200Z"
    }
   },
   "id": "ef78ad2617581e5e"
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [],
   "source": [
    "# 特征工程必须转换成字典\n",
    "trainX = trainX.to_dict(orient=\"records\")\n",
    "testX = testX.to_dict(orient=\"records\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-30T13:34:42.285215Z",
     "start_time": "2023-12-30T13:34:42.275220100Z"
    }
   },
   "id": "c5b5bc220514ed40"
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [],
   "source": [
    "transfer = DictVectorizer()\n",
    "trainX = transfer.fit_transform(trainX)\n",
    "testX = transfer.fit_transform(testX)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-30T13:34:43.652058600Z",
     "start_time": "2023-12-30T13:34:43.629801200Z"
    }
   },
   "id": "372a731fe49b9910"
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "data": {
      "text/plain": "0.776536312849162"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# xgboost模型训练\n",
    "xg = XGBClassifier()\n",
    "xg.fit(trainX, trainY)\n",
    "xg.score(testX, testY)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-30T13:35:03.471272Z",
     "start_time": "2023-12-30T13:35:03.337350Z"
    }
   },
   "id": "edf5136486fa30e6"
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 调优"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "5da20677b5f5ba6a"
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "outputs": [],
   "source": [
    "def max_dept_tuning():\n",
    "    \"\"\"\n",
    "    对max_depth进行调优\n",
    "    :return: max_index,max_value\n",
    "    \"\"\"\n",
    "    tuning_range = range(10)\n",
    "    score = []\n",
    "    for i in tuning_range:\n",
    "        xg = XGBClassifier(eta=1, gamma=0, max_depth=i)\n",
    "        xg.fit(trainX, trainY)\n",
    "        s = xg.score(testX, testY)\n",
    "        print(\"max_depth={}\\tscore={}\".format(i, s))\n",
    "        score.append(s)\n",
    "\n",
    "    max_value = max(score)\n",
    "    max_index = score.index(max_value)\n",
    "\n",
    "    print(\"最高分={}，值={}\".format(max_index, max_value))\n",
    "\n",
    "    plt.plot(tuning_range, score)\n",
    "    plt.show()\n",
    "\n",
    "    return max_index, max_value\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-30T14:08:15.921330100Z",
     "start_time": "2023-12-30T14:08:15.909641900Z"
    }
   },
   "id": "be890efff18df675"
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "outputs": [],
   "source": [
    "def tuning():\n",
    "    \"\"\"\n",
    "    对模型进行调优\n",
    "    max_depth：默认6，深度，范围：0到无限 \n",
    "    eta：默认0.3别名learning_rage 范围：[0,1]\n",
    "    gamma：分裂最loss，别名min_split_loss，范围：0到无限\n",
    "    min_child_weight：默认1，最小叶子节点权重，范围：0到无限 \n",
    "    subsample：范围：(0,1]，典型值0.5-1，05代表平均采样，防止过拟合\n",
    "    colsample_bytree：默认1，范围：(0,1]，典型值0.5-1，控制每颗随机采样的列数占比\n",
    "    colsample_bylevel：默认1，范围：0到无限，控制每颗的每一级的每一次分裂，对列数占比。一半不用，和subsample & colsample_bytree相同 \n",
    "    lambda：默认1，别名：reg_lambda，范围：0到无限，权重L2正则化(Ridge regression类似)比较少用，但是再减少过拟合上可以挖掘更多用处\n",
    "    alpha：默认0，别名：reg_alpha，权重L1正则化和（Lasso regression类似）可以在高纬度的情况下，使算法的速度更快\n",
    "    scale_pos_weight：默认1，在各类别样本不平衡时，这个参数设定为一个正值，可以使算法更快收敛，通常可以将其设置为负样本的数目与正样本的数目的比值\n",
    "    :return: \n",
    "    \"\"\"\n",
    "    # max_depth\n",
    "    score = []\n",
    "    max_dept_range = range(10)\n",
    "    for i in max_dept_range:\n",
    "        xg = XGBClassifier(eta=1, gamma=0, max_depth=i)\n",
    "        xg.fit(trainX, trainY)\n",
    "        s = xg.score(testX, testY)\n",
    "        # print(\"max_depth={}\\tscore={}\".format(i, s))\n",
    "        score.append(s)\n",
    "    max_value = max(score)\n",
    "    max_index = score.index(max_value)\n",
    "    print(\"max_depth最高分={}，值={}\".format(max_index, max_value))\n",
    "    plt.plot(max_dept_range, score)\n",
    "    plt.show()\n",
    "    max_depth = max_index\n",
    "\n",
    "    # eta\n",
    "    score = []\n",
    "    eta_range = np.arange(0.1, 1, 0.1)\n",
    "    for i in eta_range:\n",
    "        xg = XGBClassifier(eta=i, gamma=0, max_depth=max_depth)\n",
    "        xg.fit(trainX, trainY)\n",
    "        s = xg.score(testX, testY)\n",
    "        score.append(s)\n",
    "    max_value = max(score)\n",
    "    max_index = score.index(max_value)\n",
    "    print(\"eta最高分={}，值={}\".format(max_index, max_value))\n",
    "    plt.plot(eta_range, score)\n",
    "    plt.show()\n",
    "    eta = eta_range[max_index]\n",
    "\n",
    "    # gamma\n",
    "    score = []\n",
    "    gamma_range = range(10)\n",
    "    for i in gamma_range:\n",
    "        xg = XGBClassifier(eta=eta, gamma=i, max_depth=max_depth)\n",
    "        xg.fit(trainX, trainY)\n",
    "        s = xg.score(testX, testY)\n",
    "        score.append(s)\n",
    "    max_value = max(score)\n",
    "    max_index = score.index(max_value)\n",
    "    print(\"gamma最高分={}，值={}\".format(max_index, max_value))\n",
    "    plt.plot(gamma_range, score)\n",
    "    plt.show()\n",
    "    gamma = max_index\n",
    "    \n",
    "    # min_chile_weight\n",
    "    score = []\n",
    "    min_chile_weight_range = range(10)\n",
    "    for i in min_chile_weight_range:\n",
    "        xg = XGBClassifier(eta=eta, gamma=gamma, max_depth=max_depth,min_chile_weight=i)\n",
    "        xg.fit(trainX, trainY)\n",
    "        s = xg.score(testX, testY)\n",
    "        score.append(s)\n",
    "    max_value = max(score)\n",
    "    max_index = score.index(max_value)\n",
    "    print(\"min_chile_weight最高分={}，值={}\".format(max_index, max_value))\n",
    "    plt.plot(min_chile_weight_range, score)\n",
    "    plt.show()\n",
    "    min_chile_weight = max_index\n",
    "    \n",
    "    # subsample\n",
    "    score = []\n",
    "    subsample_range = np.arange(0.1, 1, 0.1)\n",
    "    for i in subsample_range:\n",
    "        xg = XGBClassifier(eta=eta, gamma=gamma, max_depth=max_depth,min_chile_weight=min_chile_weight,subsample=i)\n",
    "        xg.fit(trainX, trainY)\n",
    "        s = xg.score(testX, testY)\n",
    "        score.append(s)\n",
    "    max_value = max(score)\n",
    "    max_index = score.index(max_value)\n",
    "    print(\"subsample最高分={}，值={}\".format(max_index, max_value))\n",
    "    plt.plot(subsample_range, score)\n",
    "    plt.show()\n",
    "    subsample = subsample_range[max_index]\n",
    "    \n",
    "    print(\"最后优化结果max_depth={},eta={},gamma={},min_chile_weight={},subsample={}\".format(max_depth,eta,gamma,min_chile_weight,subsample))\n",
    "    return max_depth,eta,gamma,min_chile_weight,subsample\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-30T15:17:09.226012900Z",
     "start_time": "2023-12-30T15:17:09.177145Z"
    }
   },
   "id": "c5ae7d1bbd4fc214"
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max_depth=0\tscore=0.7877094972067039\n",
      "max_depth=1\tscore=0.7374301675977654\n",
      "max_depth=2\tscore=0.770949720670391\n",
      "max_depth=3\tscore=0.7932960893854749\n",
      "max_depth=4\tscore=0.7932960893854749\n",
      "max_depth=5\tscore=0.776536312849162\n",
      "max_depth=6\tscore=0.7821229050279329\n",
      "max_depth=7\tscore=0.7877094972067039\n",
      "max_depth=8\tscore=0.7932960893854749\n",
      "max_depth=9\tscore=0.7877094972067039\n",
      "最高分=3，值=0.7932960893854749\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "(3, 0.7932960893854749)"
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_dept_tuning()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-30T14:08:18.533994600Z",
     "start_time": "2023-12-30T14:08:17.385408400Z"
    }
   },
   "id": "417b7c4cc7fe56b"
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max_depth最高分=3，值=0.7932960893854749\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "eta最高分=8，值=0.7877094972067039\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gamma最高分=0，值=0.7877094972067039\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\xgboost\\core.py:160: UserWarning: [23:17:14] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:742: \n",
      "Parameters: { \"min_chile_weight\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "C:\\Users\\Administrator\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\xgboost\\core.py:160: UserWarning: [23:17:14] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:742: \n",
      "Parameters: { \"min_chile_weight\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "C:\\Users\\Administrator\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\xgboost\\core.py:160: UserWarning: [23:17:14] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:742: \n",
      "Parameters: { \"min_chile_weight\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "C:\\Users\\Administrator\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\xgboost\\core.py:160: UserWarning: [23:17:14] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:742: \n",
      "Parameters: { \"min_chile_weight\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "C:\\Users\\Administrator\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\xgboost\\core.py:160: UserWarning: [23:17:14] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:742: \n",
      "Parameters: { \"min_chile_weight\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "C:\\Users\\Administrator\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\xgboost\\core.py:160: UserWarning: [23:17:15] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:742: \n",
      "Parameters: { \"min_chile_weight\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "C:\\Users\\Administrator\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\xgboost\\core.py:160: UserWarning: [23:17:15] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:742: \n",
      "Parameters: { \"min_chile_weight\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "C:\\Users\\Administrator\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\xgboost\\core.py:160: UserWarning: [23:17:15] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:742: \n",
      "Parameters: { \"min_chile_weight\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "C:\\Users\\Administrator\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\xgboost\\core.py:160: UserWarning: [23:17:15] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:742: \n",
      "Parameters: { \"min_chile_weight\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "C:\\Users\\Administrator\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\xgboost\\core.py:160: UserWarning: [23:17:15] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:742: \n",
      "Parameters: { \"min_chile_weight\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min_chile_weight最高分=0，值=0.7877094972067039\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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iigEAgGnCOmFJTEyU1WqVx+MJ6fd4PEpKShpzTnl5uR5//HFt3LhRkpSamiqfz6dNmzZp586dk1pTkmw2m2w2WzjlAwCAKSqsE5bo6GhlZGSEXEDr9/vldruVk5Mz5pxr164pKir0baxWqyQpEAhMak0AADCzhHXCIkkul0tFRUXKzMxUdna2qqur5fP5VFxcLEkqLCzUwoULVVVVJUnKy8vTvn37tHz5cjkcDl28eFHl5eXKy8sLBpcbrQkAAGa2sANLQUGBBgYGVFFRob6+PqWnp6upqSl40Wx3d3fIicquXbtksVi0a9cuXb58WfPmzVNeXp727Nlz02sCAICZLeznsJiK57AAADD13JbnsAAAAEQCgQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYLxJBZba2lqlpKQoJiZGDodDra2t445dtWqVLBbLdW3t2rXBMR6PR0888YQWLFigWbNmac2aNfrVr341mdIAAMA0FHZgaWhokMvlUmVlpdrb25WWlqbc3Fz19/ePOb6xsVG9vb3B1tHRIavVqnXr1kmSAoGA8vPzdenSJb3++ut65513tGTJEjmdTvl8vt9vdwAAYFqwBAKBQDgTHA6HsrKyVFNTI0ny+/1KTk7W1q1bVVpaesP51dXVqqioUG9vr2bPnq0LFy5o6dKl6ujo0L333htcMykpSXv37tXGjRtvqi6v16uEhAQNDQ0pPj4+nC0BAIAIudnv77BOWEZGRtTW1ian0/nJAlFRcjqdamlpuak16urqtH79es2ePVuSNDw8LEmKiYkJWdNms6m5uXncdYaHh+X1ekMaAACYnsIKLIODgxodHZXdbg/pt9vt6uvru+H81tZWdXR0hJya3HPPPVq8eLHKysr029/+ViMjI3rxxRf161//Wr29veOuVVVVpYSEhGBLTk4OZysAAGAK+VTvEqqrq1Nqaqqys7ODfXfeeacaGxt14cIFzZkzR7NmzdLPf/5zPfTQQ4qKGr+8srIyDQ0NBVtPT8+nsQUAABABd4QzODExUVarVR6PJ6Tf4/EoKSlpwrk+n0/19fV6/vnnr3stIyND586d09DQkEZGRjRv3jw5HA5lZmaOu57NZpPNZgunfAAAMEWFdcISHR2tjIwMud3uYJ/f75fb7VZOTs6Ec48cOaLh4WFt2LBh3DEJCQmaN2+efvWrX+ntt9/WI488Ek55AABgmgrrhEWSXC6XioqKlJmZqezsbFVXV8vn86m4uFiSVFhYqIULF6qqqipkXl1dnfLz8zV37tzr1jxy5IjmzZunxYsX691339XTTz+t/Px8rV69epLbAgAA00nYgaWgoEADAwOqqKhQX1+f0tPT1dTUFLwQt7u7+7prT7q6utTc3KwTJ06MuWZvb69cLpc8Ho/uuusuFRYWqry8fBLbAQAA01HYz2ExFc9hAQBg6rnZ7++wT1hmkkAgoA8+Go10GQAAGCH2TqssFktE3pvAMoEPPhrVsoqfRroMAACM8N7zuZoVHZnowF9rBgAAxuOEZQKxd1r13vO5kS4DAAAjxN5pjdh7E1gmYLFYInb0BQAAPsFPQgAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYLxJBZba2lqlpKQoJiZGDodDra2t445dtWqVLBbLdW3t2rXBMVevXlVJSYkWLVqk2NhYLVu2TAcOHJhMaQAAYBoKO7A0NDTI5XKpsrJS7e3tSktLU25urvr7+8cc39jYqN7e3mDr6OiQ1WrVunXrgmNcLpeampr0gx/8QJ2dndq2bZtKSkr0xhtvTH5nAABg2gg7sOzbt0/f+ta3VFxcHDwJmTVrlg4ePDjm+Dlz5igpKSnYTp48qVmzZoUEln/5l39RUVGRVq1apZSUFG3atElpaWkTntwAAICZI6zAMjIyora2Njmdzk8WiIqS0+lUS0vLTa1RV1en9evXa/bs2cG+FStW6I033tDly5cVCAT085//XBcuXNDq1avHXWd4eFherzekAQCA6SmswDI4OKjR0VHZ7faQfrvdrr6+vhvOb21tVUdHhzZu3BjS/9JLL2nZsmVatGiRoqOjtWbNGtXW1urBBx8cd62qqiolJCQEW3JycjhbAQAAU8inepdQXV2dUlNTlZ2dHdL/0ksv6cyZM3rjjTfU1tam7373u9qyZYv+6Z/+ady1ysrKNDQ0FGw9PT23u3wAABAhd4QzODExUVarVR6PJ6Tf4/EoKSlpwrk+n0/19fV6/vnnQ/o/+OAD7dixQ0ePHg3eOXT//ffr3Llz+qu/+quQn5/+P5vNJpvNFk75AABgigrrhCU6OloZGRlyu93BPr/fL7fbrZycnAnnHjlyRMPDw9qwYUNI/0cffaSPPvpIUVGhpVitVvn9/nDKAwAA01RYJyzS725BLioqUmZmprKzs1VdXS2fz6fi4mJJUmFhoRYuXKiqqqqQeXV1dcrPz9fcuXND+uPj4/XlL39Zzz77rGJjY7VkyRKdPn1a3//+97Vv377fY2sAAGC6CDuwFBQUaGBgQBUVFerr61N6erqampqCF+J2d3dfd1rS1dWl5uZmnThxYsw16+vrVVZWpscee0z/9V//pSVLlmjPnj3avHnzJLYEAACmG0sgEAhEuohbwev1KiEhQUNDQ4qPj490OQAA4Cbc7Pc3f0sIAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADDepAJLbW2tUlJSFBMTI4fDodbW1nHHrlq1ShaL5bq2du3a4JixXrdYLPrOd74zmfIAAMA0E3ZgaWhokMvlUmVlpdrb25WWlqbc3Fz19/ePOb6xsVG9vb3B1tHRIavVqnXr1gXH/P/Xe3t7dfDgQVksFn3zm9+c/M4AAMC0YQkEAoFwJjgcDmVlZammpkaS5Pf7lZycrK1bt6q0tPSG86urq1VRUaHe3l7Nnj17zDH5+fm6cuWK3G73Tdfl9XqVkJCgoaEhxcfH3/Q8AAAQOTf7/R3WCcvIyIja2trkdDo/WSAqSk6nUy0tLTe1Rl1dndavXz9uWPF4PDp27JiefPLJCdcZHh6W1+sNaQAAYHoKK7AMDg5qdHRUdrs9pN9ut6uvr++G81tbW9XR0aGNGzeOO+bQoUOKi4vTN77xjQnXqqqqUkJCQrAlJyff3CYAAMCU86neJVRXV6fU1FRlZ2ePO+bgwYN67LHHFBMTM+FaZWVlGhoaCraenp5bXS4AADDEHeEMTkxMlNVqlcfjCen3eDxKSkqacK7P51N9fb2ef/75ccf84he/UFdXlxoaGm5Yi81mk81mu7nCAQDAlBbWCUt0dLQyMjJCLob1+/1yu93KycmZcO6RI0c0PDysDRs2jDumrq5OGRkZSktLC6csAAAwzYX9k5DL5dKrr76qQ4cOqbOzU0899ZR8Pp+Ki4slSYWFhSorK7tuXl1dnfLz8zV37twx1/V6vTpy5MiE17cAAICZKayfhCSpoKBAAwMDqqioUF9fn9LT09XU1BS8ELe7u1tRUaE5qKurS83NzTpx4sS469bX1ysQCOjP/uzPwi0JAABMc2E/h8VUPIcFAICp57Y8hwUAACASCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAONNKrDU1tYqJSVFMTExcjgcam1tHXfsqlWrZLFYrmtr164NGdfZ2ak/+ZM/UUJCgmbPnq2srCx1d3dPpjwAADDNhB1YGhoa5HK5VFlZqfb2dqWlpSk3N1f9/f1jjm9sbFRvb2+wdXR0yGq1at26dcEx//Zv/6aVK1fqnnvu0alTp/TLX/5S5eXliomJmfzOAADAtGEJBAKBcCY4HA5lZWWppqZGkuT3+5WcnKytW7eqtLT0hvOrq6tVUVGh3t5ezZ49W5K0fv163Xnnnfq7v/u7SWzhd7xerxISEjQ0NKT4+PhJrwMAAD49N/v9HdYJy8jIiNra2uR0Oj9ZICpKTqdTLS0tN7VGXV2d1q9fHwwrfr9fx44d0xe+8AXl5uZq/vz5cjgc+tGPfjThOsPDw/J6vSENAABMT2EFlsHBQY2Ojsput4f02+129fX13XB+a2urOjo6tHHjxmBff3+/rl69qhdeeEFr1qzRiRMn9PWvf13f+MY3dPr06XHXqqqqUkJCQrAlJyeHsxUAADCFfKp3CdXV1Sk1NVXZ2dnBPr/fL0l65JFHtH37dqWnp6u0tFRf+9rXdODAgXHXKisr09DQULD19PTc9voBAEBkhBVYEhMTZbVa5fF4Qvo9Ho+SkpImnOvz+VRfX68nn3zyujXvuOMOLVu2LKT/i1/84oR3CdlsNsXHx4c0AAAwPYUVWKKjo5WRkSG32x3s8/v9crvdysnJmXDukSNHNDw8rA0bNly3ZlZWlrq6ukL6L1y4oCVLloRTHgAAmKbuCHeCy+VSUVGRMjMzlZ2drerqavl8PhUXF0uSCgsLtXDhQlVVVYXMq6urU35+vubOnXvdms8++6wKCgr04IMP6itf+Yqampr04x//WKdOnZrcrgAAwLQSdmApKCjQwMCAKioq1NfXp/T0dDU1NQUvxO3u7lZUVOjBTVdXl5qbm3XixIkx1/z617+uAwcOqKqqSt/+9re1dOlS/fCHP9TKlSsnsSUAADDdhP0cFlPxHBYAAKae2/IcFgAAgEggsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGG9SgaW2tlYpKSmKiYmRw+FQa2vruGNXrVoli8VyXVu7dm1wzBNPPHHd62vWrJlMaQAAYBq6I9wJDQ0NcrlcOnDggBwOh6qrq5Wbm6uuri7Nnz//uvGNjY0aGRkJ/vv9999XWlqa1q1bFzJuzZo1eu2114L/ttls4ZYGAACmqbBPWPbt26dvfetbKi4u1rJly3TgwAHNmjVLBw8eHHP8nDlzlJSUFGwnT57UrFmzrgssNpstZNxnPvOZye0IAABMO2EFlpGREbW1tcnpdH6yQFSUnE6nWlpabmqNuro6rV+/XrNnzw7pP3XqlObPn6+lS5fqqaee0vvvvz/hOsPDw/J6vSENAABMT2EFlsHBQY2Ojsput4f02+129fX13XB+a2urOjo6tHHjxpD+NWvW6Pvf/77cbrdefPFFnT59Wg899JBGR0fHXauqqkoJCQnBlpycHM5WAADAFBL2NSy/j7q6OqWmpio7Ozukf/369cH/nZqaqvvvv1+f+9zndOrUKX31q18dc62ysjK5XK7gv71eL6EFAIBpKqwTlsTERFmtVnk8npB+j8ejpKSkCef6fD7V19frySefvOH7fPazn1ViYqIuXrw47hibzab4+PiQBgAApqewAkt0dLQyMjLkdruDfX6/X263Wzk5ORPOPXLkiIaHh7Vhw4Ybvs+vf/1rvf/++7rrrrvCKQ8AAExTYd8l5HK59Oqrr+rQoUPq7OzUU089JZ/Pp+LiYklSYWGhysrKrptXV1en/Px8zZ07N6T/6tWrevbZZ3XmzBn9x3/8h9xutx555BF9/vOfV25u7iS3BQAAppOwr2EpKCjQwMCAKioq1NfXp/T0dDU1NQUvxO3u7lZUVGgO6urqUnNzs06cOHHdelarVb/85S916NAh/fd//7cWLFig1atXa/fu3TyLBQAASJIsgUAgEOkibgWv16uEhAQNDQ1xPQsAAFPEzX5/87eEAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOPdEekCbpVAICBJ8nq9Ea4EAADcrP/73v6/7/HxTJvAcuXKFUlScnJyhCsBAADhunLlihISEsZ93RK4UaSZIvx+v37zm98oLi5OFovllq3r9XqVnJysnp4excfH37J1MTl8HubhMzELn4dZ+DxuLBAI6MqVK1qwYIGiosa/UmXanLBERUVp0aJFt239+Ph4/mMzCJ+HefhMzMLnYRY+j4lNdLLyf7joFgAAGI/AAgAAjEdguQGbzabKykrZbLZIlwLxeZiIz8QsfB5m4fO4dabNRbcAAGD64oQFAAAYj8ACAACMR2ABAADGI7AAAADjEVhuoLa2VikpKYqJiZHD4VBra2ukS5qRqqqqlJWVpbi4OM2fP1/5+fnq6uqKdFn4Xy+88IIsFou2bdsW6VJmrMuXL2vDhg2aO3euYmNjlZqaqrfffjvSZc1Yo6OjKi8v1913363Y2Fh97nOf0+7du2/493IwPgLLBBoaGuRyuVRZWan29nalpaUpNzdX/f39kS5txjl9+rS2bNmiM2fO6OTJk/roo4+0evVq+Xy+SJc247311lv63ve+p/vvvz/SpcxYv/3tb/XAAw/ozjvv1D/+4z/qvffe03e/+1195jOfiXRpM9aLL76ol19+WTU1Ners7NSLL76ov/zLv9RLL70U6dKmLG5rnoDD4VBWVpZqamok/e7vFSUnJ2vr1q0qLS2NcHUz28DAgObPn6/Tp0/rwQcfjHQ5M9bVq1f1pS99Sfv379df/MVfKD09XdXV1ZEua8YpLS3VP//zP+sXv/hFpEvB//ra174mu92uurq6YN83v/lNxcbG6gc/+EEEK5u6OGEZx8jIiNra2uR0OoN9UVFRcjqdamlpiWBlkKShoSFJ0pw5cyJcycy2ZcsWrV27NuT/J/j0vfHGG8rMzNS6des0f/58LV++XK+++mqky5rRVqxYIbfbrQsXLkiS/vVf/1XNzc166KGHIlzZ1DVt/vjhrTY4OKjR0VHZ7faQfrvdrvPnz0eoKki/O+natm2bHnjgAd13332RLmfGqq+vV3t7u956661IlzLjXbp0SS+//LJcLpd27Niht956S9/+9rcVHR2toqKiSJc3I5WWlsrr9eqee+6R1WrV6Oio9uzZo8ceeyzSpU1ZBBZMOVu2bFFHR4eam5sjXcqM1dPTo6efflonT55UTExMpMuZ8fx+vzIzM7V3715J0vLly9XR0aEDBw4QWCLkH/7hH/T3f//3Onz4sO69916dO3dO27Zt04IFC/hMJonAMo7ExERZrVZ5PJ6Qfo/Ho6SkpAhVhZKSEv3kJz/Rm2++qUWLFkW6nBmrra1N/f39+tKXvhTsGx0d1ZtvvqmamhoNDw/LarVGsMKZ5a677tKyZctC+r74xS/qhz/8YYQqwrPPPqvS0lKtX79ekpSamqr//M//VFVVFYFlkriGZRzR0dHKyMiQ2+0O9vn9frndbuXk5ESwspkpEAiopKRER48e1c9+9jPdfffdkS5pRvvqV7+qd999V+fOnQu2zMxMPfbYYzp37hxh5VP2wAMPXHeb/4ULF7RkyZIIVYRr164pKir0K9Zqtcrv90eooqmPE5YJuFwuFRUVKTMzU9nZ2aqurpbP51NxcXGkS5txtmzZosOHD+v1119XXFyc+vr6JEkJCQmKjY2NcHUzT1xc3HXXD82ePVtz587luqII2L59u1asWKG9e/fqT//0T9Xa2qpXXnlFr7zySqRLm7Hy8vK0Z88eLV68WPfee6/eeecd7du3T3/+538e6dKmrgAm9NJLLwUWL14ciI6ODmRnZwfOnDkT6ZJmJEljttdeey3SpeF/ffnLXw48/fTTkS5jxvrxj38cuO+++wI2my1wzz33BF555ZVIlzSjeb3ewNNPPx1YvHhxICYmJvDZz342sHPnzsDw8HCkS5uyeA4LAAAwHtewAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGC8/wHEBjksto17eQAAAABJRU5ErkJggg=="
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\xgboost\\core.py:160: UserWarning: [23:17:15] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:742: \n",
      "Parameters: { \"min_chile_weight\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "C:\\Users\\Administrator\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\xgboost\\core.py:160: UserWarning: [23:17:15] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:742: \n",
      "Parameters: { \"min_chile_weight\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "C:\\Users\\Administrator\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\xgboost\\core.py:160: UserWarning: [23:17:15] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:742: \n",
      "Parameters: { \"min_chile_weight\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "C:\\Users\\Administrator\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\xgboost\\core.py:160: UserWarning: [23:17:15] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:742: \n",
      "Parameters: { \"min_chile_weight\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "C:\\Users\\Administrator\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\xgboost\\core.py:160: UserWarning: [23:17:15] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:742: \n",
      "Parameters: { \"min_chile_weight\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "C:\\Users\\Administrator\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\xgboost\\core.py:160: UserWarning: [23:17:15] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:742: \n",
      "Parameters: { \"min_chile_weight\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "C:\\Users\\Administrator\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\xgboost\\core.py:160: UserWarning: [23:17:15] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:742: \n",
      "Parameters: { \"min_chile_weight\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "C:\\Users\\Administrator\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\xgboost\\core.py:160: UserWarning: [23:17:15] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:742: \n",
      "Parameters: { \"min_chile_weight\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n",
      "C:\\Users\\Administrator\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\xgboost\\core.py:160: UserWarning: [23:17:16] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:742: \n",
      "Parameters: { \"min_chile_weight\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "subsample最高分=5，值=0.8100558659217877\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最后优化结果max_depth=3,eta=0.9,gamma=0,min_chile_weight=0,subsample=0.6\n"
     ]
    },
    {
     "data": {
      "text/plain": "(3, 0.9, 0, 0, 0.6)"
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuning()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-30T15:17:16.359067900Z",
     "start_time": "2023-12-30T15:17:12.243524800Z"
    }
   },
   "id": "6700eeac5b744016"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   },
   "id": "d9c7ddc063a7fa62"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
